Kaize Ding - RankMe

163 papers A* 30A 21B 4Misc 1Journal 86Unranked 21

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Survey of Deep Graph Learning under Distribution Shifts: From Graph Out-of-Distribution Generalization to Adaptation. ACM Trans. Knowl. Discov. Data Kexin Zhang, Shuhan Liu, Song Wang, Weili Shi, Chen Chen, Pan Li, Sheng Li, Jundong Li, Kaize Ding
2026	J	`jnl`	Addressing Overthinking in Large Vision-Language Models via Gated Perception-Reasoning Optimization. CoRR Xingjian Diao, Zheyuan Liu, Chunhui Zhang, Weiyi Wu, Keyi Kong, Lin Shi, Kaize Ding, Soroush Vosoughi, Jiang Gui
2026	J	`jnl`	From Observations to States: Latent Time Series Forecasting. CoRR Jie Yang, Yifan Hu, Yuante Li, Kexin Zhang, Kaize Ding, Philip S. Yu
2026	J	`jnl`	Generalizing GNNs with Tokenized Mixture of Experts. CoRR Xiaoguang Guo, Zehong Wang, Jiazheng Li, Shawn Spitzel, Qi Yang, Kaize Ding, Jundong Li, Chuxu Zhang
2026	J	`jnl`	GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models. CoRR Oscar Rivera, Ziqing Wang, Matthieu Dagommer, Abhishek Pandey, Kaize Ding
2026	J	`jnl`	RAPTOR: Ridge-Adaptive Logistic Probes. CoRR Ziqi Gao, Yaotian Zhu, Qingcheng Zeng, Xu Zhao, Ziqing Wang, Feng Ruan, Kaize Ding
2026	A	`conf`	Rigorizing Retrieval-augmented Generation with Structured Knowledge Intelligence (6 Hrs). WSDM Zhisheng Qi, Yongjia Lei, Haoyu Han, Harry Shomer, Kaize Ding, Yu Zhang, Ryan A. Rossi, Hui Liu, Yu Wang
2026	J	`jnl`	Survey of uncertainty estimation in LLMs - Sources, methods, applications, and challenges. Inf. Fusion Jianfeng He, Linlin Yu, Changbin Li, Runing Yang, Fanglan Chen, Kangshuo Li, Min Zhang, Shuo Lei, Xuchao Zhang, Mohammad Beigi, Kaize Ding, Bei Xiao, Lifu Huang, Feng Chen, Ming Jin, Chang-Tien Lu
2026	A*	`conf`	Towards Acyclic Preference Evaluation of Language Models via Multiple Evaluators. AAAI Zhengyu Hu, Jieyu Zhang, Zhihan Xiong, Alexander Ratner, Kaize Ding, Ranjay Krishna
2025	J	`jnl`	A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization. CoRR Ziqing Wang, Kexin Zhang, Zihan Zhao, Yibo Wen, Abhishek Pandey, Han Liu, Kaize Ding
2025	J	`jnl`	A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments. CoRR Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong
2025	—	`conf`	A Survey on Model Extraction Attacks and Defenses for Large Language Models. KDD (2) Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong
2025	J	`jnl`	A Survey on Model Extraction Attacks and Defenses for Large Language Models. CoRR Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong
2025	J	`jnl`	A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives. CoRR Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong
2025	—	`conf`	AD-LLM: Benchmarking Large Language Models for Anomaly Detection. ACL (Findings) Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao
2025	—	`conf`	ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations. NAACL (Long Papers) Yichuan Li, Xinyang Zhang, Chenwei Zhang, Mao Li, Tianyi Liu, Pei Chen, Yifan Gao, Kyumin Lee, Kaize Ding, Zhengyang Wang, Zhihan Zhang, Jingbo Shang, Xian Li, Trishul Chilimbi
2025	—	`conf`	AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering. EMNLP (Findings) Ziqing Wang, Chengsheng Mao, Xiaole Wen, Yuan Luo, Kaize Ding
2025	J	`jnl`	AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering. CoRR Ziqing Wang, Chengsheng Mao, Xiaole Wen, Yuan Luo, Kaize Ding
2025	—	`conf`	Avoiding Copyright Infringement via Large Language Model Unlearning. NAACL (Findings) Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, Eric Wong
2025	J	`jnl`	Beyond Sharp Minima: Robust LLM Unlearning via Feedback-Guided Multi-Point Optimization. CoRR Wenhan Wu, Zheyuan Liu, Chongyang Gao, Ren Wang, Kaize Ding
2025	—	`conf`	Cross-Domain Conditional Diffusion Models for Time Series Imputation. ECML/PKDD (8) Kexin Zhang, Baoyu Jing, K. Selçuk Candan, Dawei Zhou, Qingsong Wen, Han Liu, Kaize Ding
2025	J	`jnl`	Cross-Domain Conditional Diffusion Models for Time Series Imputation. CoRR Kexin Zhang, Baoyu Jing, K. Selçuk Candan, Dawei Zhou, Qingsong Wen, Han Liu, Kaize Ding
2025	J	`jnl`	Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection. CoRR Junjun Pan, Yixin Liu, Rui Miao, Kaize Ding, Yu Zheng, Quoc Viet Hung Nguyen, Alan Wee-Chung Liew, Shirui Pan
2025	—	`conf`	Explaining Length Bias in LLM-Based Preference Evaluations. EMNLP (Findings) Zhengyu Hu, Linxin Song, Jieyu Zhang, Zheyuan Xiao, Tianfu Wang, Zhengyu Chen, Nicholas Jing Yuan, Jianxun Lian, Kaize Ding, Hui Xiong
2025	B	`conf`	Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? COLING Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
2025	A	`conf`	Fact or Facsimile? Evaluating the Factual Robustness of Modern Retrievers. CIKM Haoyu Wu, Qingcheng Zeng, Kaize Ding
2025	J	`jnl`	Fact or Facsimile? Evaluating the Factual Robustness of Modern Retrievers. CoRR Haoyu Wu, Qingcheng Zeng, Kaize Ding
2025	A	`conf`	Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models. WSDM Kexin Zhang, Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Kaize Ding, Xiuqiang He, Xue Liu
2025	J	`jnl`	Glocal Information Bottleneck for Time Series Imputation. CoRR Jie Yang, Kexin Zhang, Guibin Zhang, Philip S. Yu, Kaize Ding
2025	J	`jnl`	LEGO-Learn: Label-Efficient Graph Open-Set Learning. Trans. Mach. Learn. Res. Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Mengyuan Li, Kaize Ding, Yue Zhao
2025	—	`conf`	Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey. NAACL (Findings) Ruiyao Xu, Kaize Ding
2025	A*	`conf`	On Large Language Model Continual Unlearning. ICLR Chongyang Gao, Lixu Wang, Kaize Ding, Chenkai Weng, Xiao Wang, Qi Zhu
2025	J	`jnl`	POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization. CoRR Ziqing Wang, Yibo Wen, William Pattie, Xiao Luo, Weimin Wu, Jerry Yao-Chieh Hu, Abhishek Pandey, Han Liu, Kaize Ding
2025	J	`jnl`	Population-Aligned Persona Generation for LLM-based Social Simulation. CoRR Zhengyu Hu, Zheyuan Xiao, Max Xiong, Yuxuan Lei, Tianfu Wang, Jianxun Lian, Kaize Ding, Ziang Xiao, Nicholas Jing Yuan, Xing Xie
2025	—	`conf`	RelKD 2025: The Third International Workshop on Resource-Efficient Learning for Knowledge Discovery. KDD (2) Chuxu Zhang, Kaize Ding, Jundong Li, Dongkuan Xu, Haoyu Wang, Derek Zhiyuan Cheng, Huan Liu
2025	—	`conf`	RelWeb 2025: The International Workshop on Resource-Efficient Learning for the Web. WWW (Companion Volume) Chuxu Zhang, Kaize Ding, Jundong Li, Dongkuan Xu, Haoyu Wang, Derek Zhiyuan Cheng, Huan Liu
2025	—	`conf`	Resource-Efficient Learning for the Web. WWW (Companion Volume) Chuxu Zhang, Kaize Ding, Jundong Li, Dongkuan Xu, Haoyu Wang, Derek Zhiyuan Cheng, Huan Liu
2025	J	`jnl`	Revisiting Multivariate Time Series Forecasting with Missing Values. CoRR Jie Yang, Yifan Hu, Kexin Zhang, Luyang Niu, Yushun Dong, Philip S. Yu, Kaize Ding
2025	J	`jnl`	Synthetic Clinical Notes for Rare ICD Codes: A Data-Centric Framework for Long-Tail Medical Coding. CoRR Truong Vo, Weiyi Wu, Kaize Ding
2025	J	`jnl`	Task Vectors, Learned Not Extracted: Performance Gains and Mechanistic Insight. CoRR Haolin Yang, Hakaze Cho, Kaize Ding, Naoya Inoue
2025	J	`jnl`	Topology-Aware Conformal Prediction for Stream Networks. CoRR Jifan Zhang, Fangxin Wang, Philip S. Yu, Kaize Ding, Shixiang Zhu
2025	A	`conf`	Uncertainty Quantification for Multiple-Choice Questions is Just One-Token Deep. CIKM Qingcheng Zeng, Mingyu Jin, Qinkai Yu, Zhenting Wang, Wenyue Hua, Guangyan Sun, Yanda Meng, Shiqing Ma, Qifan Wang, Felix Juefei-Xu, Fan Yang, Kaize Ding, Ruixiang Tang, Yongfeng Zhang
2025	Misc	`conf`	Uncertainty-Aware Robust Learning on Noisy Graphs. ICASSP Shuyi Chen, Kaize Ding, Shixiang Zhu
2025	A*	`conf`	Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark. ICLR Yili Wang, Yixin Liu, Xu Shen, Chenyu Li, Rui Miao, Kaize Ding, Ying Wang, Shirui Pan, Xin Wang
2024	J	`jnl`	A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation. CoRR Kexin Zhang, Shuhan Liu, Song Wang, Weili Shi, Chen Chen, Pan Li, Sheng Li, Jundong Li, Kaize Ding
2024	J	`jnl`	AD-LLM: Benchmarking Large Language Models for Anomaly Detection. CoRR Tiankai Yang, Yi Nian, Shawn Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xiaohu You, Yue Zhao
2024	J	`jnl`	Avoiding Copyright Infringement via Machine Unlearning. CoRR Guangyao Dou, Zheyuan Liu, Qing Lyu, Kaize Ding, Eric Wong
2024	J	`jnl`	Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs. CoRR Shuhan Liu, Kaize Ding
2024	A	`conf`	Data Quality-aware Graph Machine Learning. CIKM Yu Wang, Kaize Ding, Xiaorui Liu, Jian Kang, Ryan A. Rossi, Tyler Derr
2024	A*	`conf`	Data-Efficient Graph Learning. AAAI Kaize Ding
2024	J	`jnl`	Data-efficient graph learning: Problems, progress, and prospects. AI Mag. Kaize Ding, Yixin Liu, Chuxu Zhang, Jianling Wang
2024	A*	`conf`	Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise. KDD Kaize Ding, Xiaoxiao Ma, Yixin Liu, Shirui Pan
2024	—	`conf`	Empowering Large Language Models for Textual Data Augmentation. ACL (Findings) Yichuan Li, Kaize Ding, Jianling Wang, Kyumin Lee
2024	J	`jnl`	Empowering Large Language Models for Textual Data Augmentation. CoRR Yichuan Li, Kaize Ding, Jianling Wang, Kyumin Lee
2024	J	`jnl`	Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers? CoRR Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
2024	J	`jnl`	Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models. CoRR Kexin Zhang, Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Kaize Ding, Xiuqiang He, Xue (Steve) Liu
2024	J	`jnl`	Generalized few-shot node classification: toward an uncertainty-based solution. Knowl. Inf. Syst. Zhe Xu, Kaize Ding, Yu-Xiong Wang, Huan Liu, Hanghang Tong
2024	A*	`conf`	Graph Anomaly Detection with Few Labels: A Data-Centric Approach. KDD Xiaoxiao Ma, Ruikun Li, Fanzhen Liu, Kaize Ding, Jian Yang, Jia Wu
2024	J	`jnl`	LEGO-Learn: Label-Efficient Graph Open-Set Learning. CoRR Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Kaize Ding, Yue Zhao
2024	J	`jnl`	Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey. CoRR Ruiyao Xu, Kaize Ding
2024	—	`conf`	Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning. EMNLP (Findings) Zhengyu Hu, Yichuan Li, Zhengyu Chen, Jingang Wang, Han Liu, Kyumin Lee, Kaize Ding
2024	J	`jnl`	Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning. CoRR Zhengyu Hu, Yichuan Li, Zhengyu Chen, Jingang Wang, Han Liu, Kyumin Lee, Kaize Ding
2024	A	`conf`	MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders. WACV Zhangsihao Yang, Kaize Ding, Huan Liu, Yalin Wang
2024	A*	`conf`	Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization. KDD Haohui Wang, Baoyu Jing, Kaize Ding, Yada Zhu, Wei Cheng, Si Zhang, Yonghui Fan, Liqing Zhang, Dawei Zhou
2024	B	`conf`	MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection. DSAA Xiongxiao Xu, Kaize Ding, Canyu Chen, Kai Shu
2024	J	`jnl`	Multitask Active Learning for Graph Anomaly Detection. CoRR Wenjing Chang, Kay Liu, Kaize Ding, Philip S. Yu, Jianjun Yu
2024	A*	`conf`	On Fake News Detection with LLM Enhanced Semantics Mining. EMNLP Xiaoxiao Ma, Yuchen Zhang, Kaize Ding, Jian Yang, Jia Wu, Hao Fan
2024	J	`jnl`	Political-LLM: Large Language Models in Political Science. CoRR Lincan Li, Jiaqi Li, Catherine Chen, Fred Gui, Hongjia Yang, Chenxiao Yu, Zhengguang Wang, Jianing Cai, Junlong Aaron Zhou, Bolin Shen, Alex Qian, Weixin Chen, Zhongkai Xue, Lichao Sun, Lifang He, Hanjie Chen, Kaize Ding, Zijian Du, Fangzhou Mu, Jiaxin Pei, Jieyu Zhao, Swabha Swayamdipta, Willie Neiswanger, Hua Wei, Xiyang Hu, Shixiang Zhu, Tianlong Chen, Yingzhou Lu, Yang Shi, Lianhui Qin, Tianfan Fu, Zhengzhong Tu, Yuzhe Yang, Jaemin Yoo, Jiaheng Zhang, Ryan A. Rossi, Liang Zhan, Liang Zhao, Emilio Ferrara, Yan Liu, Furong Huang, Xiangliang Zhang, Lawrence Rothenberg, Shuiwang Ji, Philip S. Yu, Yue Zhao, Yushun Dong
2024	A*	`conf`	RelKD 2024: The Second International Workshop on Resource-Efficient Learning for Knowledge Discovery. KDD Chuxu Zhang, Dongkuan Xu, Kaize Ding, Jundong Li, Mojan Javaheripi, Subhabrata Mukherjee, Nitesh V. Chawla, Huan Liu
2024	A*	`conf`	Revisiting Score Propagation in Graph Out-of-Distribution Detection. NeurIPS Longfei Ma, Yiyou Sun, Kaize Ding, Zemin Liu, Fei Wu
2024	J	`jnl`	Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification. ACM Trans. Knowl. Discov. Data Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, Huan Liu
2024	J	`jnl`	RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response. CoRR Junyu Luo, Xiao Luo, Kaize Ding, Jingyang Yuan, Zhiping Xiao, Ming Zhang
2024	A*	`conf`	Sterling: Synergistic Representation Learning on Bipartite Graphs. AAAI Baoyu Jing, Yuchen Yan, Kaize Ding, Chanyoung Park, Yada Zhu, Huan Liu, Hanghang Tong
2024	J	`jnl`	TSI-Bench: Benchmarking Time Series Imputation. CoRR Wenjie Du, Jun Wang, Linglong Qian, Yiyuan Yang, Fanxing Liu, Zepu Wang, Zina M. Ibrahim, Haoxin Liu, Zhiyuan Zhao, Yingjie Zhou, Wenjia Wang, Kaize Ding, Yuxuan Liang, B. Aditya Prakash, Qingsong Wen
2024	A	`conf`	The 5th International Workshop on Machine Learning on Graphs (MLoG). WSDM Tyler Derr, Yao Ma, Kaize Ding, Tong Zhao, Nesreen K. Ahmed
2024	J	`jnl`	Toward Robust Graph Semi-Supervised Learning Against Extreme Data Scarcity. IEEE Trans. Neural Networks Learn. Syst. Kaize Ding, Elnaz Nouri, Guoqing Zheng, Huan Liu, Ryen White
2024	J	`jnl`	Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models. CoRR Qingcheng Zeng, Mingyu Jin, Qinkai Yu, Zhenting Wang, Wenyue Hua, Zihao Zhou, Guangyan Sun, Yanda Meng, Shiqing Ma, Qifan Wang, Felix Juefei-Xu, Kaize Ding, Fan Yang, Ruixiang Tang, Yongfeng Zhang
2024	J	`jnl`	Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark. CoRR Yili Wang, Yixin Liu, Xu Shen, Chenyu Li, Kaize Ding, Rui Miao, Ying Wang, Shirui Pan, Xin Wang
2023	J	`jnl`	Characterizing Long-Tail Categories on Graphs. CoRR Haohui Wang, Baoyu Jing, Kaize Ding, Yada Zhu, Dawei Zhou
2023	A*	`conf`	Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning. AAAI Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu
2023	A*	`conf`	Federated Few-shot Learning. KDD Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li
2023	J	`jnl`	Federated Few-shot Learning. CoRR Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li
2023	A	`conf`	Few-shot Node Classification with Extremely Weak Supervision. WSDM Song Wang, Yushun Dong, Kaize Ding, Chen Chen, Jundong Li
2023	J	`jnl`	Few-shot Node Classification with Extremely Weak Supervision. CoRR Song Wang, Yushun Dong, Kaize Ding, Chen Chen, Jundong Li
2023	A	`conf`	GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection. WSDM Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan
2023	—	`conf`	GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs. EMNLP (Findings) Yichuan Li, Kaize Ding, Kyumin Lee
2023	J	`jnl`	GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs. CoRR Yichuan Li, Kaize Ding, Kyumin Lee
2023	A*	`conf`	HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer. SIGIR Kaize Ding, Albert Jiongqian Liang, Bryan Perozzi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng
2023	J	`jnl`	HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer. CoRR Kaize Ding, Albert Jiongqian Liang, Bryan Perozzi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng
2023	A*	`conf`	Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation. NeurIPS Zhangsihao Yang, Mengwei Ren, Kaize Ding, Guido Gerig, Yalin Wang
2023	J	`jnl`	Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation. CoRR Zhangsihao Yang, Mengwei Ren, Kaize Ding, Guido Gerig, Yalin Wang
2023	A	`conf`	Learning Node Abnormality with Weak Supervision. CIKM Qinghai Zhou, Kaize Ding, Huan Liu, Hanghang Tong
2023	A*	`conf`	Learning Strong Graph Neural Networks with Weak Information. KDD Yixin Liu, Kaize Ding, Jianling Wang, Vincent C. S. Lee, Huan Liu, Shirui Pan
2023	J	`jnl`	Learning Strong Graph Neural Networks with Weak Information. CoRR Yixin Liu, Kaize Ding, Jianling Wang, Vincent C. S. Lee, Huan Liu, Shirui Pan
2023	J	`jnl`	MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection. CoRR Xiongxiao Xu, Kaize Ding, Canyu Chen, Kai Shu
2023	J	`jnl`	STERLING: Synergistic Representation Learning on Bipartite Graphs. CoRR Baoyu Jing, Yuchen Yan, Kaize Ding, Chanyoung Park, Yada Zhu, Huan Liu, Hanghang Tong
2023	A	`conf`	STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction. CIKM Paras Sheth, Ahmadreza Mosallanezhad, Kaize Ding, Reepal Shah, John Sabo, Huan Liu, K. Selçuk Candan
2023	A*	`conf`	Towards Self-Interpretable Graph-Level Anomaly Detection. NeurIPS Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan
2023	J	`jnl`	Towards Self-Interpretable Graph-Level Anomaly Detection. CoRR Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan
2023	J	`jnl`	UPREVE: An End-to-End Causal Discovery Benchmarking System. CoRR Suraj Jyothi Unni, Paras Sheth, Kaize Ding, Huan Liu, K. Selçuk Candan
2023	J	`jnl`	Uncertainty-Aware Robust Learning on Noisy Graphs. CoRR Shuyi Chen, Kaize Ding, Shixiang Zhu
2023	A*	`conf`	Virtual Node Tuning for Few-shot Node Classification. KDD Zhen Tan, Ruocheng Guo, Kaize Ding, Huan Liu
2023	J	`jnl`	Virtual Node Tuning for Few-shot Node Classification. CoRR Zhen Tan, Ruocheng Guo, Kaize Ding, Huan Liu
2022	J	`jnl`	A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning. CoRR Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu
2022	A*	`conf`	BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs. NeurIPS Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu
2022	J	`jnl`	Benchmarking Node Outlier Detection on Graphs. CoRR Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu
2022	B	`conf`	Causal Disentanglement with Network Information for Debiased Recommendations. SISAP Paras Sheth, Ruocheng Guo, Kaize Ding, Lu Cheng, K. Selçuk Candan, Huan Liu
2022	—	`conf`	Classifying COVID-19 Related Meta Ads Using Discourse Representation Through a Hypergraph. SBP-BRiMS Ujun Jeong, Zeyad Alghamdi, Kaize Ding, Lu Cheng, Baoxin Li, Huan Liu
2022	J	`jnl`	Cross-Domain Graph Anomaly Detection. IEEE Trans. Neural Networks Learn. Syst. Kaize Ding, Kai Shu, Xuan Shan, Jundong Li, Huan Liu
2022	J	`jnl`	Data Augmentation for Deep Graph Learning: A Survey. CoRR Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu
2022	J	`jnl`	Data Augmentation for Deep Graph Learning: A Survey. SIGKDD Explor. Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu
2022	A*	`conf`	Few-Shot Learning on Graphs. IJCAI Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu
2022	J	`jnl`	Few-Shot Learning on Graphs: A Survey. CoRR Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu
2022	J	`jnl`	GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection. CoRR Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan
2022	A*	`conf`	Generalized Few-Shot Node Classification. ICDM Zhe Xu, Kaize Ding, Yu-Xiong Wang, Huan Liu, Hanghang Tong
2022	A	`conf`	Graph Few-shot Class-incremental Learning. WSDM Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu
2022	A	`conf`	Graph Minimally-supervised Learning. WSDM Kaize Ding, Jundong Li, Nitesh V. Chawla, Huan Liu
2022	J	`jnl`	Learning with Few Labeled Nodes via Augmented Graph Self-Training. CoRR Kaize Ding, Elnaz Nouri, Guoqing Zheng, Huan Liu, Ryen White
2022	A*	`conf`	Meta Propagation Networks for Graph Few-shot Semi-supervised Learning. AAAI Kaize Ding, Jianling Wang, James Caverlee, Huan Liu
2022	B	`conf`	Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks. IEEE Big Data Ujun Jeong, Kaize Ding, Lu Cheng, Ruocheng Guo, Kai Shu, Huan Liu
2022	J	`jnl`	Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks. CoRR Ujun Jeong, Kaize Ding, Lu Cheng, Ruocheng Guo, Kai Shu, Huan Liu
2022	J	`jnl`	PyGOD: A Python Library for Graph Outlier Detection. CoRR Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, George H. Chen, Zhihao Jia, Philip S. Yu
2022	J	`jnl`	Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning. CoRR Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu
2022	—	`conf`	Supervised Graph Contrastive Learning for Few-Shot Node Classification. ECML/PKDD (2) Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu
2022	A*	`conf`	Task-Adaptive Few-shot Node Classification. KDD Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, Jundong Li
2022	J	`jnl`	Task-Adaptive Few-shot Node Classification. CoRR Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, Jundong Li
2022	A*	`conf`	Toward Graph Minimally-Supervised Learning. KDD Kaize Ding, Chuxu Zhang, Jie Tang, Nitesh V. Chawla, Huan Liu
2022	—	`conf`	Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification. LoG Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu
2022	J	`jnl`	Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification. CoRR Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu
2021	A	`conf`	AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter. CIKM Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, Jundong Li
2021	J	`jnl`	FBAdTracker: An Interactive Data Collection and Analysis Tool for Facebook Advertisements. CoRR Ujun Jeong, Kaize Ding, Huan Liu
2021	A*	`conf`	Fact-Enhanced Synthetic News Generation. AAAI Kai Shu, Yichuan Li, Kaize Ding, Huan Liu
2021	A*	`conf`	Few-shot Network Anomaly Detection via Cross-network Meta-learning. WWW Kaize Ding, Qinghai Zhou, Hanghang Tong, Huan Liu
2021	J	`jnl`	Few-shot Network Anomaly Detection via Cross-network Meta-learning. CoRR Kaize Ding, Qinghai Zhou, Hanghang Tong, Huan Liu
2021	—	`conf`	GLOW : Global Weighted Self-Attention Network for Web Search. IEEE BigData Xuan Shan, Chuanjie Liu, Yiqian Xia, Qi Chen, Yusi Zhang, Kaize Ding, Yaobo Liang, Angen Luo, Yuxiang Luo
2021	J	`jnl`	Graph Few-shot Class-incremental Learning. CoRR Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu
2021	J	`jnl`	Graph Neural Networks with Adaptive Frequency Response Filter. CoRR Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, Jundong Li
2021	—	`conf`	Learning to Selectively Learn for Weakly-supervised Paraphrase Generation. EMNLP (1) Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, Huan Liu
2021	J	`jnl`	Learning to Selectively Learn for Weakly-supervised Paraphrase Generation. CoRR Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, Huan Liu
2021	J	`jnl`	Meta Propagation Networks for Graph Few-shot Semi-supervised Learning. CoRR Kaize Ding, Jianling Wang, James Caverlee, Huan Liu
2021	A*	`conf`	Sequential Recommendation for Cold-start Users with Meta Transitional Learning. SIGIR Jianling Wang, Kaize Ding, James Caverlee
2021	J	`jnl`	Sequential Recommendation for Cold-start Users with Meta Transitional Learning. CoRR Jianling Wang, Kaize Ding, James Caverlee
2021	A	`conf`	Session-based Recommendation with Hypergraph Attention Networks. SDM Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee
2021	J	`jnl`	Session-based Recommendation with Hypergraph Attention Networks. CoRR Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee
2021	A	`conf`	Towards Anomaly-resistant Graph Neural Networks via Reinforcement Learning. CIKM Kaize Ding, Xuan Shan, Huan Liu
2021	J	`jnl`	Weakly-supervised Graph Meta-learning for Few-shot Node Classification. CoRR Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, Huan Liu
2020	—	`conf`	Be More with Less: Hypergraph Attention Networks for Inductive Text Classification. EMNLP (1) Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu
2020	J	`jnl`	Be More with Less: Hypergraph Attention Networks for Inductive Text Classification. CoRR Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu
2020	—	`conf`	Challenges in Combating COVID-19 Infodemic - Data, Tools, and Ethics. CIKM (Workshops) Kaize Ding, Kai Shu, Yichuan Li, Amrita Bhattacharjee, Huan Liu
2020	J	`jnl`	Challenges in Combating COVID-19 Infodemic - Data, Tools, and Ethics. CoRR Kaize Ding, Kai Shu, Yichuan Li, Amrita Bhattacharjee, Huan Liu
2020	J	`jnl`	Combating Disinformation in a Social Media Age. CoRR Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora H. Nazer, Kaize Ding, Mansooreh Karami, Huan Liu
2020	J	`jnl`	Combating disinformation in a social media age. WIREs Data Mining Knowl. Discov. Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora H. Nazer, Kaize Ding, Mansooreh Karami, Huan Liu
2020	J	`jnl`	Fact-Enhanced Synthetic News Generation. CoRR Kai Shu, Yichuan Li, Kaize Ding, Huan Liu
2020	A	`conf`	Graph Few-shot Learning with Attribute Matching. CIKM Ning Wang, Minnan Luo, Kaize Ding, Lingling Zhang, Jundong Li, Qinghua Zheng
2020	A	`conf`	Graph Prototypical Networks for Few-shot Learning on Attributed Networks. CIKM Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, Huan Liu
2020	J	`jnl`	Graph Prototypical Networks for Few-shot Learning on Attributed Networks. CoRR Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, Huan Liu
2020	A*	`conf`	Inductive Anomaly Detection on Attributed Networks. IJCAI Kaize Ding, Jundong Li, Nitin Agarwal, Huan Liu
2020	A	`conf`	Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion. WSDM Jianling Wang, Kaize Ding, Ziwei Zhu, Yin Zhang, James Caverlee
2020	A*	`conf`	Next-item Recommendation with Sequential Hypergraphs. SIGIR Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, James Caverlee
2019	A	`conf`	Deep Anomaly Detection on Attributed Networks. SDM Kaize Ding, Jundong Li, Rohit Bhanushali, Huan Liu
2019	J	`jnl`	Graph Neural Networks with High-order Feature Interactions. CoRR Kaize Ding, Yichuan Li, Jundong Li, Chenghao Liu, Huan Liu
2019	A*	`conf`	InterSpot: Interactive Spammer Detection in Social Media. IJCAI Kaize Ding, Jundong Li, Shivam Dhar, Shreyash Devan, Huan Liu
2019	A	`conf`	Interactive Anomaly Detection on Attributed Networks. WSDM Kaize Ding, Jundong Li, Huan Liu